Education | Experience | Community | Publications | Talks
2011–2016
- PhD Structural Biology
- Adviser: Vijay Pande
- Internships at Google and Vertex Pharmaceuticals
- Dissertation: "Finding Needles in a Haystack: Molecular Similarity and Machine Learning for Drug Discovery Applications"
2004, 2007–2011
- BS Biochemistry magna cum laude
- Advisers: Steven Herron, Barry Willardson
2021–Present: Director, AI Research
2019–2021: Senior Research Scientist
2018–2019: Research Scientist
2016–2018: Software Engineer
- Discussion Leader, "Next-Generation Machine Learning in Drug Discovery", Computer Aided Drug Design Gordon Research Conference (2023)
- External Advisory Board, NSF Center for Computer Assisted Synthesis (C-CAS) (2022–Present)
- Editorial Board, Artificial Intelligence in the Life Sciences (2021–Present)
- Guest Editor, "Artificial Intelligence in Drug Discovery", Journal of Medicinal Chemistry Special Issue (2020)
- Governing Committee, Open Reaction Database (2019–Present)
- A new database for machine-learning research (C&EN, 22 November 2021)
- Machine Learning On Top of DNA Encoded Libraries (In the Pipeline, 16 June 2020)
- Unlocking the "Chemome" with DNA-Encoded Chemistry and Machine Learning (Google AI Blog, 11 June 2020)
- Neural Networks for Drug Discovery: A Work in Progress (In the Pipeline, 4 March 2015)
- Large-Scale Machine Learning for Drug Discovery (Google AI Blog, 2 March 2015)
- Mercado R, Kearnes SM, Coley C. Data Sharing in Chemistry: Lessons Learned and a Case for Mandating Structured Reaction Data. J Chem Inf Model 63, 14, 4253–4265. (JCIM)
- Goldman B, Kearnes S, Kramer T, Riley P, Walters WP. Defining Levels of Automated Chemical Design. J Med Chem 65, 10, 7073–7087. (JMC)
- Kearnes SM, Maser MR, Wleklinski M, Kast A, Doyle AG, Dreher SD, Hawkins JM, Jensen KF, Coley CW. The Open Reaction Database. JACS 143(45), 18820-18826. (JACS)
- Kearnes S. Pursuing a Prospective Perspective. Trends in Chemistry 3, 77–79. (TiC, arXiv)
- Sun R, Dai H, Li L, Kearnes S, Dai B. Energy-Based View of Retrosynthesis. (arXiv)
- Bajorath J, Kearnes S, Walters WP, Meanwell NA, Georg GI, Wang S. Artificial Intelligence in Drug Discovery: Into the Great Wide Open. J Med Chem 63, 8651–8652. (JMC)
- McCloskey K, Sigel EA, Kearnes S, Xue L, Tian X, Moccia D, et al. Machine Learning on DNA-Encoded Libraries: A New Paradigm for Hit Finding. J Med Chem 63, 8857–8866. (JMC, arXiv)
- Moshiri J, Constant DA, Liu B, Mateo R, Kearnes S, Novick P, et al. A Targeted Computational Screen of the SWEETLEAD Database Reveals FDA-Approved Compounds with Anti-Dengue Viral Activity. mBio 11. (mBio)
- Zhou Z, Kearnes S, Li L, Zare RN, Riley P. Optimization of Molecules via Deep Reinforcement Learning. Scientific Reports 9, 10752. (SciRep)
- Bajorath J, Kearnes S, Walters WP, Georg GI, Wang S. The Future is Now: Artificial Intelligence in Drug Discovery. J Med Chem 62, 5249. (JMC)
- Kearnes S, Li L, Riley P. Decoding Molecular Graph Embeddings with Reinforcement Learning. ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Data. (arXiv)
- Thomas N, Smidt T, Kearnes S, Yang L, Li L, Kohlhoff K, et al. Tensor Field Networks: Rotation- and Translation-Equivariant Neural Networks for 3D Point Clouds. (arXiv)
- Faber FA, Hutchison L, Huang B, Gilmer J, Schoenholz SS, Dahl GE, Vinyals O, Kearnes S, et al. Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error. J Chem Theory Comput 13, 5255–5264. (JCTC, arXiv)
- T. McGibbon R, X. Hernández C, P. Harrigan M, Kearnes S, M. Sultan M, Jastrzebski S, et al. Osprey: Hyperparameter Optimization for Machine Learning. J Open Source Softw 1, 34. (JOSS)
- Kearnes S, Goldman B, Pande V. Modeling Industrial ADMET Data with Multitask Networks. (arXiv)
- Kearnes S, Pande V. ROCS-Derived Features for Virtual Screening. J Comput Aided Mol Des 30, 609–617. (JCAMD, arXiv)
- Kearnes S, McCloskey K, Berndl M, Pande V, Riley P. Molecular Graph Convolutions: Moving Beyond Fingerprints. J Comput Aided Mol Des 30, 595–608. (JCAMD, arXiv)
- Ramsundar B, Kearnes S, Riley P, Webster D, Konerding D, Pande V. Massively Multitask Networks for Drug Discovery. (arXiv)
- Kearnes SM, Haque IS, Pande VS. SCISSORS: Practical Considerations. J Chem Inf Model 54, 5–15. (JCIM, PubMed)
- MIT Biotech Group (15 March)
- Harvard COMPSCI 282R: Topics in Machine Learning: Advancements in Probabilistic ML, Programming Models for ML, Causality (8 April)
- Harvard APMTH 216: Inverse Problems in Science and Engineering (6 April)
- C-CAS Semi-Annual Meeting (11 March; remote)
- VISTA Symposium on Artificial-Intelligence and Data-Science assisted Synthesis (16 July; remote)
- Cambridge Cheminformatics Meeting (2 June; YouTube)
- NIH Virtual Workshop on Reaction Informatics (19 May; video)
- SynTech CDT, University of Cambridge, Cambridge, UK (12 May; remote)
- Boston Area Group for Informatics and Modeling (26 January; YouTube)
- Bay Area COMP Together (17 November; remote lightning talk)
- RDKit UGM (7 October; YouTube)
- Nurix Therapeutics (29 June; remote)
- Harvard APMTH 216: Inverse Problems in Science and Engineering (3 April; remote)
- Insitro, South San Francisco, CA (12 March)
- AI Powered Drug Discovery and Manufacturing Conference, MIT, Cambridge, MA (27 February; panel discussion)
- Relay Therapeutics, Cambridge, MA (25 February)
- Gordon Research Conference: Computer Aided Drug Design, West Dover, VT (16 July)
- Central New Jersey Data Science Meetup, Monmouth Junction, NJ (21 July; remote)
- OpenEye CUP, Santa Fe, NM (7 March)
- SPARK at Stanford (4 October; panel discussion)
- Vertex Pharmaceuticals, Abingdon, UK (23 May; remote)
- Vertex Pharmaceuticals, Boston, MA (1 May)
- NSF CHE Workshop: Framing the Role of Big Data and Modern Data Science in Chemistry, Arlington, VA (18 April)
- Novartis Institutes for BioMedical Research, Basel, Switzerland (6 April)
- O. Anatole von Lilienfeld research group, University of Basel, Basel, Switzerland (6 April)
- GTCBio Drug Design and Medicinal Chemistry Conference, Boston, MA (12 September)
- Brigham Young University Department of Chemistry and Biochemistry, Provo, UT (23 September)
- National Institute of Environmental Health Sciences, Research Triangle Park, NC (16 September; remote)
- Novartis Institutes for Biomedical Research, Cambridge, MA (6 August; remote)
- Gordon Research Seminar: Computer Aided Drug Design, West Dover, VT (18 July)
- Williams College Physics Department, Williamstown, MA (7 November)
- Vertex Pharmaceuticals, Boston, MA (6 November)
- F5 Networks, Spokane, WA (23 June)